SoroushVahidi/scidocs huggingface dataset:
A New Aggregate Local Mobility (ALM) Clustering Algorithm for VANETs
(2010)

We present a beacon-based clustering algorithm aimed at prolonging the cluster lifetime in VANETs. We use a new aggregate local mobility criterion to decide upon cluster re-organisation. The scheme incorporates a contention method to avoid triggering frequent re-organisations when two clusterheads encounter each other for a short period of time. Simulation results show a significant improvement of cluster lifetime and reduced node state/role changes compared to previous popular clustering algorithms.
Kinematic analysis of electroactive polymer actuators as soft and smart structures with more DoF than inputs
(2012)

Electroactive polymer (EAP) actuators have been attracting the attention of researchers due to their muscle-like behaviour and unusual properties. Several modelling methods have been proposed to understand their mechanical, chemical, electrical behaviours or `electro-chemo-mechanical' behaviour. However, estimating the whole shape or configuration of the EAP actuators has always been challenging due to their highly non-linear bending behaviour. This paper reports on an effective method to estimate the whole shape deflection of the EAP actuators by employing a so-called backbone approach. Tri-layer configured polypyrrole (PPy) based EAP actuators were used as a soft and smart structure with more degrees of freedom than its input. After deriving the inverse kinematic model of the actuator, its complete shape is estimated by solving the inverse kinematic model with an angle optimization (AngleOPT) method. The experimental results and numerical results have demonstrated the effectiveness of the method in estimating the highly non-linear bending behaviour of the PPy actuators and applicability of this modelling approach to other EAP actuators.
A Bidirectional Bridge Modular Switched-Capacitor-Based Power Electronics Transformer
(2018)

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.
Symbol Interdependency in Symbolic and Embodied Cognition
(2011)

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.
Recognition and volume estimation of food intake using a mobile device
(2009)

We present a system that improves accuracy of food intake assessment using computer vision techniques. Traditional dietetic method suffers from the drawback of either inaccurate assessment or complex lab measurement. Our solution is to use a mobile phone to capture images of foods, recognize food types, estimate their respective volumes and finally return quantitative nutrition information. Automated and accurate food recognition presents the following challenges. First, there exist a large variety of food types that people consume in everyday life. Second, a single category of food may contain large variations due to different ways of preparation. Also, diverse lighting conditions may lead to varying visual appearance of foods. All of these pose a challenge to the state of the art recognition approaches. Moreover, the low quality images captured using cellphones make the task of 3D reconstruction difficult. In this paper, we combine several vision techniques (visual recognition and 3D reconstruction) to achieve quantitative food intake estimation. Evaluation of both recognition and reconstruction is provided in the experimental results.
Automatic Array Alignment in Data-Parallel Programs
(1993)

Data-parallel languages like Fortran 90 express parallelism in the form of operations on data aggregates such as arrays. Misalignment of the operands of an array operation can reduce program performance on a distributed-memory parallel machine by requiring nonlocal data accesses. Determining array alignments that reduce communication is therefore a key issue in compiling such languages.
We present a framework for the automatic determination of array alignments in data-parallel languages such as Fortran 90. Our language model handles array sectioning, reductions, spreads, transpositions, and masked operations. We decompose alignment functions into three constituents: axis, stride, and offset. For each of these subproblems, we show how to solve the alignment problem for a basic block of code, possibly containing common subexpressions. Alignments are generated for all array objects in the code, both named program variables and intermediate results. The alignments obtained by our algorithms are more general than those provided by the “owner-computes” rule. Finally, we present some ideas for dealing with control flow, replication, and dynamic alignments that depend on loop induction variables.
Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome.
(2000)

Methods for assessment, e.g., anthropometric indicators and imaging techniques, of several phenotypes of human obesity, with special reference to abdominal fat content, have been evaluated. The correlation of fat distribution with age, gender, total body fat, energy balance, adipose tissue lipoprotein lipase and lipolytic activity, adipose tissue receptors, and genetic characteristics are discussed. Several secreted or expressed factors in the adipocyte are evaluated in the context of fat tissue localization. The body fat distribution and the metabolic profile in nonobese and obese individuals is discussed relative to lipolysis, antilypolysis and lipogenesis, insulin sensitivity, and glucose, lipid, and protein metabolism. Finally, the endocrine regulation of abdominal visceral fat in comparison with the adipose tissue localized in other areas is presented.
The language of mental health problems in social media
(2016)

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.
CipherXRay: Exposing Cryptographic Operations and Transient Secrets from Monitored Binary Execution
(2014)

Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.
DDoS: threats and mitigation
(2011)

According to a report from Prolexic, one of the first and largest companies offering DDoS mitigation services, ⁠ attack traffic rose 66% over the course of a year (to Q3 2011). 1 Network-layer attacks accounted for 83%, the rest being application-layer attacks. The average duration was 1.4 days and the average bandwidth consumed was 1.5Gbps. The size of attacks is getting bigger, too. In July 2011, Prolexic announced it had mitigated what it believed to be the largest packet-per-second DDoS attack ever seen in Asia. Consisting of SYN and ICMP floods, the attack deployed 176,000 bots (compared to the 5,000-10,000 bots more normally seen by Prolexic) to generate 25 million packets per second. According to the company, the majority of high-end border rout-ers typically forward 70,000 packets per second. It mitigated the attack by distributing traffic among its Tier 1 carrier partners and scrubbing centres. Ben Petro, senior VP network intelligence & availability at Verisign, traces the rise of DDoS attacks back another year. He says that for years there was little awareness of the problem but that, " 2010 was a dramatic shift – not only in the size, scale and trajectory of DDoS, but also in its proliferation and the number of different types of organisation that were hit. " In the years 2006-2008, he adds, the average attack was somewhere around 40Mbps. " And then, all of a sudden, coming in 2010 we started to see 2Gbps, then 5Gbps, then 8Gbps and 15Gbps attacks, culminating in the largest that we've seen coming in at 84Gbps sustained for a week and a half. That is an incredible amount of traffic when you get down to packets or queries per second – you're over the hundreds of thousands of queries per second per location for Verisign, and we have 165 locations. So it's an enormous volume. " The rising popularity of DDoS attacks may be connected directly to their effectiveness. Paul Sop, CTO at Prolexic, says, " people get creative and they use and invent new ways and reasons to use DDoS. When an idea catches on in the industry there's a tipping point – people say, hey here's a type of attack we can launch and it's very likely we won't go to jail unless we're pretty dumb about it. " He adds: " It is like asymmetric warfare – the attackers have so much advantage in terms …
Software-defined optical networks (SDONs): a survey
(2014)

This paper gives an overview of software-defined optical networks (SDONs). It explains the general concepts on software-defined networks (SDNs), their relationship with network function virtualization, and also about OpenFlow, which is a pioneer protocol for SDNs. It then explains the benefits and challenges of extending SDNs to multilayer optical networks, including flexible grid and elastic optical networks, and how it compares to generalized multi-protocol label switching for implementing a unified control plane. An overview on the industry and research efforts on SDON standardization and implementation is given next, to bring the reader up to speed with the current state of the art in this field. Finally, the paper outlines the benefits achieved by SDONs for network operators, and also some of the important and relevant research problems that need to be addressed.
OCR for bilingual documents using language modeling
(2015)

Script based features are highly discriminative for text segmentation and recognition. Thus they are widely used in Optical Character Recognition(OCR) problems. But usage of script dependent features restricts the adaptation of such architectures directly for another script. With script independent systems, this problem can be solved to a certain extent for monolingual documents. But the problem aggravates in case of multilingual documents as it is very difficult for a single classifier to learn many scripts. Generally a script identification module identifies text segments and accordingly the script-dependent classifier is selected. This paper presents a unified framework of language model and multiple preprocessing hypotheses for word recognition from bilingual document images. Prior to text recognition, preprocessing steps such as binarization and segmentation are required for ease of recognition. But these steps induce huge combinatorial error propagating to final recognition accuracy. In this paper we use multiple preprocessing routines as alternate hypotheses and use a language model to verify each alternative and choose the best recognized sequence. We test this architecture for word recognition of Kannada-English and Telugu-English bilingual documents and achieved better recognition rates than single methods using same classifier.
A Use Case Identification Framework and Use Case Canvas for identifying and exploring relevant Blockchain opportunities
(2018)

Blockchain is a new, foundational technology with a vast amount of application possibilities. However, practitioners might not be aware of which use cases in their own business model might benefit from blockchain technology. To aid them in analyzing their business regarding blockchain suitability, this paper introduces a use case identification framework for blockchain and a use case canvas. In the development process they have been evaluated with internal and external reviews in order to offer the best possible guidance. In combination they offer an analysis framework to help practitioners decide which use cases they should take into account for blockchain technology, which characteristics these blockchain implementations would have, and which specific advantages they would offer.
Neural Adaptation Layers for Cross-domain Named Entity Recognition
(2018)

Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms stateof-the-art methods.
Boosting Optical Character Recognition: A Super-Resolution Approach
(2015)

Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset [3] and the released Tesseract-OCR 3.02 system [1]. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images (78.80%).
Preventing Cell Phone Intrusion and Theft using Biometrics
(2013)

Most cell phones use a password, PIN, or visual pattern to secure the phone. With these types of security methods being used, there is much vulnerability. Another alternative is biometric authentication. Biometric security systems have been researched for many years. Some mobile manufacturers have implemented fingerprint scanners into their phones, such as the old Fujitsu F505i [7] and the current Motorola Atrix. Since theft of cell phones is becoming more common every day, there is a real need for a security system that not only protects the data, but the phone itself. It is proposed through this research that a biometric security system be the alternative to knowledge-based and password-based authentication. Furthermore, a device dongle must be implemented into this infrastructure to establish a reliable security system that deters theft for the majority; biometrics alone is not sufficient. Cell phones need power and must be charged almost daily. A biometric phone charger that acts as a dongle with a solid state relay, will be presented as a viable solution to theft in this research. Additionally, it will be shown through the results of this research that a system dependant only on biometrics is unreliable and unsecure. Essentially, a mobile security system that combines biometrics with dongle technology is believed to be the ideal solution for limiting the black market of stolen cell phones; without the biometric charger/dongle, the stolen cell phone would be rendered useless.
Cuff-less, non-invasive and continuous blood pressure monitoring using indirect methods
(2015)

Cuff-based methods for measuring blood pressure (BP) have shown some limitations particularly when continuous measurement is required. Moreover, the inflation of the cuff is one of the main disturbing factors as expressed among patients. In this study, a promising approach based on changes in pulse transit time (PTT) for continuous, non-invasive and indirect measurement of BP is proposed to overcome the shortcomings of cuff-based methods. The PTT can be measured using plethysmography (PPG) waveform and the QRS peak of electrocardiogram (ECG) signal and indirectly interpreted as systolic BP. Time synchronization of the two sensors plays an important role in improving the accuracy of measurement. Therefore, it is aimed to employ a tablet-based system for collecting the wireless devices' data at a specific time. It is also our objective to include a triggering mechanism option using ambient sensors in order to start the process of data collection. Bluetooth/IEEE 802.15.4 wireless technologies will be used for real-time sensing and time synchronization. We have found that the proposed indirect measurement of BP can provide continuous information of the BP in a more convenient way from the patients' point of view. Moreover, the result of our indirect measurement of BP indicates that there are some short term fluctuations in the BP.
3D printed RF passive components by liquid metal filling
(2015)

We present three-dimensional (3D) micro-scale electrical components and systems by means of 3D printing and a liquid-metal-filling technique. The 3D supporting polymer structures with hollow channels and cavities are fabricated from inkjet printing. Liquid metals made of silver particles suspension in this demonstration are then injected into the hollow paths and solidified to form metallic elements and interconnects with high electrical conductivity. In the proof-of-concept demonstrations, various radio-frequency (RF) passive components, including 3D-shaped inductors, capacitors and resistors are fabricated and characterized. High-Q inductors and capacitors up to 1 GHz have been demonstrated. This work establishes an innovative way to construct arbitrary 3D electrical systems with efficient and labor-saving processes.
Strengths and Weaknesses of Quantum Computing
(1997)

Recently a great deal of attention has focused on quantum computation following a sequence of results [4, 16, 15] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor’s result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class NP ∩ co–NP cannot be solved on a quantum Turing machine in time o(2n/3). The former bound is tight since recent work of Grover [13] shows how to accept the class NP relative to any oracle on a quantum computer in time O(2n/2). To appear in SIAM Journal on Computing (special issue on quantum computing) ∗ IBM T. J. Watson Research Laboratory, Yorktown Heights, New York, NY 10598, USA. email: bennetc@watson.ibm.com. † 1 Microsoft Way, Redmond, WA 98052 – 6399, USA. email: ethanb@microsoft.com. ‡ Supported in part by Canada’s nserc and Québec’s fcar. §Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. ¶ Supported by NSF Grant No. CCR-9310214. Computer Science Division, University of California, Berkeley, CA 94720, USA. email: vazirani@cs.berkeley.edu.